{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import *\n",
    "from collections import Counter\n",
    "from skimage import exposure\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "IMAGE_DIR = 'image_contest_level_1_validate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7dc51d0390>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xm/FrVL/s4MGDa/P79++ffcWWiDuDAQAAAAAGQGcwAAAAAMAASBOxTVsd8rMX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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7dc5d4d5d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 705
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e = [629,2271,6579,17416,71857,77631,95303,102187,117422,142660,183693]\n",
    "index = e[8]\n",
    "\n",
    "img = cv2.imread('%s/%d.png' % (IMAGE_DIR, index))\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "h = cv2.equalizeHist(gray)\n",
    "# _, h = cv2.threshold(h, 127, 255, cv2.THRESH_BINARY)\n",
    "# h = cv2.dilate(h, np.ones((3, 3)))\n",
    "# h = cv2.morphologyEx(h, cv2.MORPH_CLOSE, np.ones((3, 3)))\n",
    "\n",
    "# c = Counter(h.flatten().tolist())\n",
    "# # h2 = h == c.most_common()[1][0]\n",
    "# h2 = h == sorted(c.keys())[-1]\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(2, 2, 1);plt.title('raw');plt.imshow(img[:,:,::-1])\n",
    "plt.subplot(2, 2, 2);plt.title('gray');plt.imshow(gray, cmap='gray')\n",
    "plt.subplot(2, 2, 3);plt.title('result');plt.imshow(h, cmap='gray')\n",
    "# plt.subplot(2, 2, 4);plt.title('result');plt.imshow(h2, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
